Linescan System Research Articles

Tracking the Development of Lit Fisheries by Using DMSP/OLS Data in the Open South China Sea

Nightly images offer a special data source for monitoring fishing activities. This study used images from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) to analyze the early development of lit fisheries in the open South China Sea (SCS), which mainly occurred around the Zhong Sha and Xi Sha Islands. Based on peak detection and a fixed threshold, lit fishing positions were extracted well from filtered, high-quality DMSP/OLS images. The results indicated that fisheries experienced an apparent rise and fall from 2005 to 2012, with the numbers of lit fishing boats rising to a maximum of ~60 from 2005 to 2008, almost disappearing in 2009, peaking at ~130 from 2010 to 2011, and starting to decline in 2012. The fish price of major fishing targets declined by ~60% in 2009, which obviously impacted the year’s fishing operations. The reason for declined fishing operations in 2012 was that most of the lit fishing operations shifted farther south to fishing grounds around the Nan Sha Islands. We also explored factors shaping the distribution patterns of lit fisheries by using MaxEnt models to relate fishing positions to environmental variables. Major environmental factors influencing the distribution of lit fishing boats varied with years, of which water depth was the most important factor across years, with an optimal depth range of 1000–2000 m. In addition to depth, the distribution of lit fisheries was also influenced by SST, especially for the years 2005–2008, and a suitable SST was found between 26 and 28 °C. This study fills the knowledge gaps of the inception of lit fisheries and their dynamic changes in the SCS.

Outdoor light at night is a modifiable environmental factor for metabolic syndrome: The 33 Communities Chinese Health Study (33CCHS)

Metabolic syndrome (MetS) is a significant public health problem and presents an escalating clinical challenge globally. To combat this problem effectively, urgent measures including identify some modifiable environmental factors are necessary. Outdoor artificial light at night (LAN) exposure garnered much attention due to its impact on circadian rhythms and metabolic process. However, epidemiological evidence on the association between outdoor LAN exposure and MetS remains limited. To determine the relationship between outdoor LAN exposure and MetS, 15,477 adults participated the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. Annual levels of outdoor LAN exposure at participants' residential addresses were assessed using satellite data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). Generalized linear mixed effect models were utilized to assess the association of LAN exposure with MetS and its components, including elevated waist circumference (WC), triglycerides (TG), blood pressure (BP), fasting blood glucose (FBG), and reduced high-density lipoprotein cholesterol (HDL-C). Effect modification by various social demographic and behavior factors was also examined. Overall, 4701 (30.37 %) participants were defined as MetS. The LAN exposure ranged from 6.03 to 175.00 nW/cm2/sr. The adjusted odds ratio (OR) of MetS each quartile increment of LAN exposure were 1.43 (95 % CI: 1.21–1.69), 1.44 (95 % CI: 1.19–1.74) and 1.52 (95 % CI: 1.11–2.08), respectively from Q2–Q4. Similar adverse associations were also found for the components of MetS, especially for elevated BP, TG and FBG. Interaction analyses indicated that the above associations were stronger in participants without habitual exercise compared with those with habitual exercise (e.g. OR were 1.52 [95 % CI: 1.28–1.82] vs. 1.27 [95 % CI, 1.04–1.55], P-interaction = 0.042 for MetS). These findings suggest that long-term exposure to LAN can have a significant deleterious effect on MetS, potentially making LAN an important modifiable environmental factor to target in future preventive strategies.

Which Nighttime Lights Data Better Represent India’s Economic Activities and Regional Inequality?

This paper demonstrates how the estimation of economic activities and inequality for the Indian economy based on nighttime lights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite outperforms those obtained using traditional Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) sources. In the extant literature, nighttime lights data have been frequently used as a proxy for economic activities, especially at granular geographical levels where gross domestic product data are otherwise unavailable. Since the traditional DMSP-OLS-based nighttime lights data suffer from measurement errors due to blurring and top coding, VIIRS-based nighttime lights data, which are devoid of such limitations, are gaining popularity. We show that at the subnational or district level in India, VIIRS satellite-based nighttime lights data are indeed a better proxy for economic activities and inequality. Our robust findings imply that all future scholarly works that employ nighttime lights data for the Indian economy should use VIIRS-based data for greater accuracy compared to other similar data sources.

Classification of different wheat flour types using hyperspectral imaging and machine learning techniques

Different wheat flour types are used to produce various baked products. Due to the whiteness of the four types, hyperspectral imaging can be used due to receiving infrared wavelength. The technique was applied to distinguish confectionery flour and the flours of Samoun, Sangak, and Tafton breads using a line scanning system in the range of 400–950 nm. Effective wavelengths were selected and different image features were extracted from the corresponding image channels. The selected wavelengths were 601.33, 620.34, 696.41, 730.31, 821.26, and 841.11 nm. The extracted features were used in classification step using linear discriminant analysis, support vector machine, and artificial neural network methods in MATLAB software. The classification accuracy of artificial neural network was higher than the other methods. The efficient features gave higher classification accuracy (98.1 %) than all extracted features (96.9 %). The results showed the high ability of hyperspectral imaging combined with artificial neural network to distinguish different wheat flour types.

Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

Refining Long-Time Series of Urban Built-Up-Area Extraction Based on Night-Time Light—A Case Study of the Dongting Lake Area in China

By studying the development law of urbanization, the problems of disorderly expansion and resource wastage in urban built-up areas can be effectively avoided, which is crucial for the long-term sustainable development of cities. This study proposes a high-precision urban built-up-area extraction method for county-level cities for small and medium-sized towns in county-level regions. Our process is based on the Defense Meteorological Satellite/Operational Linescan System (DMSP/OLS) and the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which develops long-term series of coordinated night-time light (NTL) datasets. We then combined this with the Normalized Vegetation Index (NDVI) to calculate the Vegetation-Adjusted NTL Urban Index (VANUI). We combine land use data and a support vector machine (SVM) for semi-supervised classification learning to propose a high-precision urban built-up-area extraction method for county-level cities. We achieved the following results: (1) we fit binary polynomials to the DMSP/OLS and VIIRS NTL datasets based on the correspondence of the mean values to construct a consistent time series of NTL data. (2) Our method effectively improves the accuracy of urban built-up-area extraction, especially for county-level cities, with an overall accuracy of 91.84% and a Kappa coefficient of 0.83. (3) Our method can perform a long-time series of urban built-up-area extraction, and, by studying the spatial and temporal changes in urban built-up areas, it can provide valuable information for sustainable urban development and urban planning.

Experimental correlation between bending crack evolution and load-drop criterion in tight-radius three-point bending test with GPa-grade steels

Determining the bending-fracture limit, also referred to as local formability, is crucial for sheet metal forming. However, this is limited by existing methods such as the VDA-238-100 standard due to limitations in its failure criterion based on the punch load. This study addresses this limitation by proposing a quantitative crack-based failure criterion and investigating its relationship to the load-drop point. Along with surface and cross-sectional optical analyses, an integrated line-scanning system for the VDA-238 test is developed, and advanced high-strength steel (AHSS) grades are analysed with yield strengths ranging from 980 to 1500 MPa in both the rolling and transverse directions. The results showed that, while the load-drop point marks a rapid shift in the crack-propagation direction, the true onset of crack initiation occurs earlier, with different patterns for each AHSS and direction. For all the AHSS and directions tested, a crack depth exceeding 1 % of the sheet thickness occurs at 90 % of the switch-off stroke in the VDA-238 test, suggesting an empirical tendency toward microcrack growth for a more accurate evaluation of the local formability based on early cracks.

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens.

Of patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival. We aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures. A hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens. A total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument. We showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.

Generation of monthly VIIRS nighttime lights time-series (1992–2013) images using deep learning (cGAN) technique

Satellite-based nighttime lights (NTL) are often used as indicators of the socioeconomic dynamics of an area. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992–December 2013). VIIRS-DNB (hereinafter referred to as VIIRS) and DMSP-OLS (hereinafter referred to as DMSP) NTL images have 15-arc second and 30-arc second spatial resolution, respectively. In the current study, an attempt has been made to generate synthetic (fine-resolution) monthly VIIRS-like products of 1992–2012 from (coarse-resolution) DMSP products, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992–2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done by thresholding. Improved monthly NTL images of DMSP-OLS and VIIRS from April 2012–December 2013 were used in a conditional generative adversarial network (cGAN) along with the land cover, as auxiliary input, to generate VIIRS-like monthly imagery from 1992 to 2012. The modelled imagery was aggregated annually, which showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products (2000–2020) over India; R2 of 0.85 w.r.t GDP; and R2 of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an R2 of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an R2 of 0.56 and 0.53 respectively. Qualitative analysis is also performed on both country and State scales. Visual analysis over 1992–2013 confirms a gradual increase in the brightness of the nighttime lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, synthetically generated monthly VIIRS-like products are delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017

BackgroundMalaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. MethodsIn this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000–17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program—Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000–16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FindingsMalaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10–4 for Pakistan, 4·86 × 10–6 for India, and 1·32 × 10–5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. InterpretationMalaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FundingNIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Tourism and marine crises: The impact of Sargassum invasion on Caribbean small island developing sates

Since 2011 the Caribbean has been experiencing large amounts of Sargassum on its beaches causing disruptions to tourism. There are nonetheless no large-scale empirical studies across the regizon on the impact of Sargassum on tourism and local economies. In addressing this knowledge gap this study investigates the impact of Sargassum on sandy beaches on tourism and sandy beach economic activity across 30 Caribbean Small Island Developing States. Spectral reflectance data from the MODIS satellite mission and a Floating Algae Index were used to detect the presence of Sargassum on sandy beaches. Tourism activity was measured using monthly tourist arrivals data from the Caribbean Tourism Organization and near sandy beaches nightlights data from the United States Air Force Defense Meteorological Satellite Program-Operational Linescan System. A Fixed effects panel regression model which controls for island specific time invariant effects, monthly and yearly time effects, and island specific time trends and allow for cross-sectional dependence and serial correlation was adopted. The results demonstrate that the average incidence of Sargassum reduces tourist arrivals growth by 1.1 percentage points up to 8 months after an outbreak with the highest incidence event having a 9 percentage points reduction. The results from nightlight intensity show a fall in sandy beach economic activity up to 6 months after an outbreak. An average Sargassum event of 0.25 and 0.15 constitute a reduction in sandy beach nightlight intensity of about 1.6 and 0.9 percent respectively.

Abstract MP77: Both Racial/Ethnic Residential Segregation and Exposure to Outdoor Artificial Light at Night May Impact Cardiovascular Health but Perhaps Not Through Self-Reported Sleep

Introduction: Due to structural racism, minoritized racial/ethnic groups are overrepresented in racially segregated areas of concentrated disadvantage in which residences may be less protective against outdoor artificial light at night (ALAN). Both racial/ethnic residential segregation (RRS) and ALAN are associated with disparities in both sleep duration, potentially through ALAN exposures in segregated areas disrupting melatonin production, and cardiovascular health (CVH). However, few studies have investigated pathways between RRS and ALAN, sleep, and CVH. We sought to investigate sleep duration as a potential mediator between RRS and ALAN exposures and CVH among US adults. Hypothesis: We hypothesized that (1) ALAN would disproportionately burden minoritized racial/ethnic groups, (2) sleep duration would mediate associations between both RRS and ALAN and CVH, and (3) associations would vary by race/ethnicity. Methods: We used data collected from 2011 National Health Interview Survey adult participants reporting sleep duration (short [<7 hours(h)]), recommended [7-9 h], long [>9 h]) and CVH (defined as yes vs. no to all: never/former smoking, BMI 18-<25 kg/m 2 , meets physical activity guidelines, and no prior diagnosis of dyslipidemia, hypertension, or pre-diabetes/diabetes). Participants’ home addresses were geographically linked to the 2012 American Community Survey 5-year estimates to assess RRS, measured by the local Getis-Ord G i * statistic (z-scores) categories: low (z<0), medium (0≤z≤1.96), and high (z>1.96). Quintiles of residential ALAN were estimated from 2010-2011 US Defense Meteorological Satellite Program’s Operational Linescan System values. We used robust Poisson regression adjusted for sociodemographic characteristics, estimated mediation through sleep duration with the Sobel’s test, and assessed effect modification by race/ethnicity. Results and Conclusions: Among 18,057 participants (mean age±SE =45±0.2 years); 35%, 44%, and 21% resided in low, medium, and highly segregated neighborhoods, respectively. ALAN was highest among non-Hispanic White adults. Prevalence of short and long sleep duration overall were 31% and 3% and highest among Black adults (39% and 4%). High (vs. low) RRS was associated with a higher prevalence of CVH (PR=1.13 [95% CI:1.01-1.26]). However, higher ALAN (Q5 vs. Q1) was associated with a lower prevalence of CVH (PR=0.79 [95% CI:0.66-0.95]). Sleep did not appear to mediate associations, which did not vary by race/ethnicity. In conclusion, self-reported sleep did not mediate associations between RRS or ALAN and CVH. Nonetheless, further study using objective measures is warranted given the biological plausibility. The findings/conclusions in this research are those of the authors and do not necessarily represent the views of the Research Data Center, National Center for Health Statistics, or CDC.

The first dark sky map of Chiang Rai : three decades of change affecting Astro-tourism

We present the first Chiang Rai dark sky map, measurements of the calibrated zenith artificial night sky brightness at sea level, which covers data since 1992–2021. The Data were collected from two US satellites including the Defense Meteorological Satellite Program with the Operational Line Scan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). Images containing ratios of artificial light to natural light were originally excluded from sunlight, solar glare, moon light, lunar illuminance, lightning features from the aurora, clouds, fires and background noise. Based on the correlation between data from the overlapped year 2013, the result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R2 = 0.92) which revealed the mathematical model was feasible and reliable. The number of dark sky areas over Chiang Rai in the past 3 decades was classified in 8 classes based on the sky brightness in Bortle scale colour code and in magnitude : 1. Excellent dark sky sites (>21.9 mag, black), 2. Dark sky (21.9-21.5 mag, grey), 3. Rural sky (21.5-21.3 mag, blue), 4. Suburban/Rural transition sky (21.3-20.1 mag, green), 5. Suburban sky (20.1-19.1 mag, yellow), 6. Bright suburban sky (19.1-19.5 mag, orange), 7. City/suburban transition sky (19.5-18.0 mag, red)and 8. City sky (<18.0 mag, white). It was found that 14% of area in Chiang Rai are covering by the bright sky with Bortle class 7 and 8. Within this only 14% of area dwell more than 33% of population that have now already lost naked eye visibility of the Milky Way. In addition, a risk assessment model for dark sky conservation revealed that dark sky area are dramatically becoming extinction. It was suggested that Chiang Rai will not have an area suitable for stargazing by 2040 if there is no designated area for conservation. Our map could ultimately encourage land owners of such dark sky sites to register their properties in the IAU dark sky reserve database to elevate Astro-tourism in Chiang Rai.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study

BackgroundUrbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.MethodsCounty-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.ResultsThe mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from − 0.205 to − 0.131.ConclusionsThe influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Multiscale spatial-temporal evolution of energy carbon footprint in the Yellow River Basin of China based on DMSP/OLS and NPP/VIIRS integrated data.

Increased CO2 emissions from urban energy consumption pose a significant challenge to regional carbon mitigation policies. In this paper, we integrated two nighttime light (NTL) data: the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) composite data to estimate the energy carbon emissions from 2000 to 2019. Then the spatiotemporal dynamics of carbon footprint and deficit in the Yellow River Basin were analyzed at the provincial, municipal, and county scales combined with NPP data. The study shows that (1) the total amount of energy consumption CO2 emissions in the Yellow River Basin had increased from 1332 Mt in 2000 to 6469 Mt in 2019, but the average annual growth rate slowed down after 2010 from 11.5 to 5.61%. (2) From 2000 to 2018, the provinces with the highest carbon footprint and carbon deficit were concentrated in Inner Mongolia and Shanxi. In 2018, Inner Mongolia's carbon footprint was 1366.91 × 104 km2, accounting for 22.8% of the total. Cities with high carbon footprint were mainly economic centers and energy-intensive areas of various provinces. High-carbon deficit counties were mainly distributed in the western region. In 2018, 954 counties exhibited carbon deficits. (3) The carbon footprint in the Yellow River Basin at the municipal and county scales have a significant spatial correlation. The H-H clusters of the carbon footprint on the municipal scale were distributed in the middle reaches of the Yellow River Basin. At the county scale, the L-L clusters were mainly in Sichuan and eastern Henan regions. Through the analysis of the spatial and temporal evolution of carbon footprint and carbon deficit in the Yellow River Basin, it is significant to measure the degree of comprehensive coordination of carbon sources and sinks in the basin, to grasp the differences in the level of regional carbon emissions, and to promote synergistic regional governance, assist in the formulation of more precise carbon emission reduction policies, and to promote green and high-quality development.

Urban polycentrism and total-factor energy efficiency: An analysis based on the night light data

Improving energy efficiency is crucial for environmental protection and resource conservation. Considerable urban system distribution is vital to realizing high energy efficiency. However, studies on energy efficiency from this spatial perspective remain limited. Using the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and visible infrared imaging radiometer suite (VIIRS) nighttime light data, we measured urban polycentrism (UPC) and employed improved stochastic frontier analysis (SFA) for total-factor energy efficiency (TEE) measurement. Analyzing panel data from 332 prefecture-level cities across 27 provinces of China, we used the spatial economic and threshold models to calculate UPC's impact on TEE and its threshold conditions. The results revealed UPC's effect on TEE and the control variables (economic development, urbanization rate, and industrial structure). The pattern suggests that only when economic development, urbanization rate, and industrial structure reach a certain (range) level in some zones, does the effect change from one condition (inhibition/promotion) to another (promotion/inhibition). Under certain conditions, a win-win situation between UPC and TEE is achieved. Finally, relevant policy recommendations for energy efficiency improvements based on urbanization are discussed. To the best of our knowledge, this is the first study to systematically investigate the impact of UPC on TEE at the city level.

Assessment of out‐of‐plane structural defects using parallel laser line scanning system

AbstractA precise parallel laser line scanning system has been developed to assess the depth of out‐of‐plane structural defects on concrete surfaces. This system comprises a digital camera, dual line laser diodes, and positioning rigid arms that create a triangulation‐based setup. Laser lines are distorted when projected onto an out‐of‐plane defect. A new image processing algorithm has been devised to extract depth information from the distorted laser strips. Parallel laser lines are used to ensure that imaged laser strips do not intersect, thereby simplifying the depth assessment of defects at different distances and enabling the generation of defect profiles from a single image. The system has been validated through laboratory and field tests, demonstrating its effectiveness and accuracy. Compared to other noncontact measurement techniques, this system stands out due to its simplicity, cost‐effectiveness, efficiency, and superior accuracy for long‐range measurements, rendering it suitable for on‐site scanning of textureless uneven engineering objects.

Spatiotemporal changes of aerosol optical depth and its response to urbanization: a case study of Jinan City, China, 2009-2018.

With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009-2013, the annual average variation of AOD increased by 20.6%, while during 2014-2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements.

Association between Outdoor Light at Night and Prostate Cancer in the Health Professionals Follow-up Study.

Circadian disruption is a potential risk factor for advanced prostate cancer, and light at night (LAN) exposure may disrupt circadian rhythms. We evaluated whether outdoor LAN increases the risk of prostate cancer. We prospectively followed 49,148 participants in the Health Professionals Follow-up Study from 1986 through 2016. We estimated baseline and cumulative time-varying outdoor LAN with ∼1 km2 resolution using data from the US Defense Meteorological Satellite Program's Operational Linescan System, which was assigned to participants' geocoded addresses. Participants reside in all 50 U.S. states and reported a work or home address. We used multivariable Cox models to estimate HRs and 95% confidence intervals (CI) for the association between outdoor LAN and risk of overall (7,175 cases) and fatal (915 cases) prostate cancer adjusting for individual and contextual factors. There was no association between the interquartile range increase in cumulative LAN and total (HR, 1.02; 95% CI, 0.98-1.06) or fatal (HR, 1.05; 95% CI, 0.96-1.15) prostate cancer in adjusted models. However, there was a positive association between baseline LAN and total prostate cancer among non-movers (HR, 1.06; 95% CI, 1.00-1.14) including among highly screened participants (HR, 1.11; 95% CI, 1.01-1.23). There was a suggestive positive association between baseline outdoor LAN and total prostate cancer. Additional studies with different measures of outdoor LAN and in more diverse populations are necessary. To our knowledge, this is the first longitudinal cohort study exploring the relationship between outdoor LAN and prostate cancer.

Monitoring Urban Expansion (2000–2020) in Yangtze River Delta Using Time-Series Nighttime Light Data and MODIS NDVI

The Yangtze River Delta Urban Agglomeration (YRDUA), which is located in the convergence zone of “The Belt and Road Initiative”, is one of the regions with the best urbanization foundations in China. Referring to the four five-year plans (China’s national economic plan), this study aimed to investigate the spatiotemporal patterns of urban expansion in the YRDUA from 2000 to 2020. To conduct a long-term analysis of urbanization, an extended time series (2000–2020) of a nighttime light (NTL) dataset was built from the multi-temporal Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data (2000–2013), and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data (2014–2020); data from these sources are crucial to understanding the urbanization processes in the region in order for more effective decision making to take place. The support vector machine (SVM) method was used to extract urban clusters from the extended time-series NTL data and MODIS NDVI products. The evolution of the urban expansion intensity was detected at city scales, and the inequality of urban growth was demonstrated using the Lorenz curve and Gini coefficient. Finally, a quantitative relationship between urban NTL intensity and socio-economic data was built to explore the main factors that control urban intensity. The results indicated that the urban extents extracted from time-series NTL data were consistent with those extracted from Landsat data, with an average overall accuracy (OA) of 89%. A relatively fast urbanization pace was observed during the 10th five-year plan (from 2000 to 2005), which then declined slightly in the 11th five-year plan (from 2006 to 2010). By the 12th and 13th five-year plan (from 2011 to 2020), urban clusters in all cities tended to grow steadily. Urban expansion has presented a radial pattern around the main cities, with sprawl inequality across cities. The results further revealed that the primary factors controlling NTL brightness were gross domestic product (GDP), total fixed asset investment, tertiary industry, gross industrial output, urban area, and urban permanent residents in city clusters, but the same driving factors had a different contribution order on the NTL intensity across cities. This study provides significant insight for further urbanization study to be conducted in the YRDUA region, which is crucial for sustainable urban development in the region.